{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1b280ab-b61f-4d1a-bf7e-44e5f9ed3a5c",
   "metadata": {
    "id": "e1b280ab-b61f-4d1a-bf7e-44e5f9ed3a5c"
   },
   "source": [
    "<table style=\"width:100%\">\n",
    "<tr>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<font size=\"2\">\n",
    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
    "</font>\n",
    "</td>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efde77f2-6af3-4781-8597-89ecd3f41a52",
   "metadata": {
    "id": "efde77f2-6af3-4781-8597-89ecd3f41a52"
   },
   "source": [
    "# Qwen3 From Scratch (A Standalone Notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cdef4d-de59-4a65-89f9-fa2a8ef3471d",
   "metadata": {
    "id": "55cdef4d-de59-4a65-89f9-fa2a8ef3471d"
   },
   "source": [
    "- This notebook is purposefully minimal and focuses on the code to implement Qwen3 0.6B, 1.7B, 4B, 8B, and 32B; for more information about this model, please see the original blog post and technical report:\n",
    "  - [Qwen3: Think Deeper, Act Faster](https://qwenlm.github.io/blog/qwen3/)\n",
    "  - [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) \n",
    "- Many architectural components in Qwen3 are similar to Llama 3; for a step-by-step guide that explains the individual components and the relationship between GPT and the components used here, you may like the GPT-to-Llama conversion notebooks:\n",
    "  - [Converting a From-Scratch GPT Architecture to Llama 2](../07_gpt_to_llama/converting-gpt-to-llama2.ipynb)\n",
    "  - [Converting Llama 2 to Llama 3.2 From Scratch](../07_gpt_to_llama/converting-llama2-to-llama3.ipynb)\n",
    "  \n",
    "\n",
    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/qwen/qwen-overview.webp\">\n",
    "  \n",
    "  \n",
    "- About the code:\n",
    "  - all code is my own code, mapping the Qwen3 architecture onto the model code implemented in my [Build A Large Language Model (From Scratch)](http://mng.bz/orYv) book; the code is released under a permissive open-source Apache 2.0 license (see [LICENSE.txt](https://github.com/rasbt/LLMs-from-scratch/blob/main/LICENSE.txt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c201adb-747e-437b-9a62-442802941e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install -r https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/refs/heads/main/ch05/07_gpt_to_llama/requirements-extra.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd1b65a8-4301-444a-bd7c-a6f2bd1df9df",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dd1b65a8-4301-444a-bd7c-a6f2bd1df9df",
    "outputId": "4f762354-e0a3-4cc2-e5d4-e61a227a202c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface_hub version: 0.34.4\n",
      "tokenizers version: 0.21.4\n",
      "torch version: 2.8.0\n"
     ]
    }
   ],
   "source": [
    "from importlib.metadata import version\n",
    "\n",
    "pkgs = [\n",
    "    \"huggingface_hub\",  # to download pretrained weights\n",
    "    \"tokenizers\",       # to implement the tokenizer\n",
    "    \"torch\",            # to implement the model\n",
    "]\n",
    "for p in pkgs:\n",
    "    print(f\"{p} version: {version(p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e96fbb-8e16-4f6d-835f-c6159321280b",
   "metadata": {},
   "source": [
    "- Note that there are two models, the \"base\" and the \"hybrid\" model, and the hybrid model can be used as either a reasoning or a regular instruction-following model:\n",
    "- In short, the model types are as follows:\n",
    "  - `base`: the pretrained base model; note that the Qwen3 pretraining contained some reasoning data (chain-of-thought data), so the model sometimes emits reasoning traces even though it didn't undergo the reasoning training (reinforcement learning) stages\n",
    "  - `hybrid` \n",
    "    - `reasoning`: emits long reasoning traces inside `<think></think>` tags\n",
    "    - `instruct`: the same as above, but long reasoning traces can be suppressed by manually adding empty `<think></think>` (this is done by the tokenizer); this way, the model acts like a regular instruction-following model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70a90338-624a-4706-aa55-6b4358070194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select which model to use via the following flag; only one can be True\n",
    "\n",
    "USE_BASE_MODEL = False\n",
    "USE_REASONING_MODEL = True \n",
    "USE_INSTRUCT_MODEL = False\n",
    "\n",
    "if (USE_BASE_MODEL + USE_REASONING_MODEL\n",
    "    + USE_INSTRUCT_MODEL) != 1:\n",
    "    raise AttributeError(\"Only one of the options above can be True.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653410a6-dd2b-4eb2-a722-23d9782e726d",
   "metadata": {
    "id": "653410a6-dd2b-4eb2-a722-23d9782e726d"
   },
   "source": [
    "&nbsp;\n",
    "# 1. Architecture code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82076c21-9331-4dcd-b017-42b046cf1a60",
   "metadata": {
    "id": "82076c21-9331-4dcd-b017-42b046cf1a60"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "class FeedForward(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(cfg[\"emb_dim\"], cfg[\"hidden_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
    "        self.fc2 = nn.Linear(cfg[\"emb_dim\"], cfg[\"hidden_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
    "        self.fc3 = nn.Linear(cfg[\"hidden_dim\"], cfg[\"emb_dim\"], dtype=cfg[\"dtype\"], bias=False)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x_fc1 = self.fc1(x)\n",
    "        x_fc2 = self.fc2(x)\n",
    "        x = nn.functional.silu(x_fc1) * x_fc2\n",
    "        return self.fc3(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56715760-37e1-433e-89da-04864c139a9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RMSNorm(nn.Module):\n",
    "    def __init__(self, emb_dim, eps=1e-6, bias=False, qwen3_compatible=True):\n",
    "        super().__init__()\n",
    "        self.eps = eps\n",
    "        self.qwen3_compatible = qwen3_compatible\n",
    "        self.scale = nn.Parameter(torch.ones(emb_dim))\n",
    "        self.shift = nn.Parameter(torch.zeros(emb_dim)) if bias else None\n",
    "\n",
    "    def forward(self, x):\n",
    "        input_dtype = x.dtype\n",
    "\n",
    "        if self.qwen3_compatible:\n",
    "            x = x.to(torch.float32)\n",
    "\n",
    "        variance = x.pow(2).mean(dim=-1, keepdim=True)\n",
    "        norm_x = x * torch.rsqrt(variance + self.eps)\n",
    "        norm_x = norm_x * self.scale\n",
    "\n",
    "        if self.shift is not None:\n",
    "            norm_x = norm_x + self.shift\n",
    "\n",
    "        return norm_x.to(input_dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b9a346f-5826-4083-9162-abd56afc03f0",
   "metadata": {
    "id": "4b9a346f-5826-4083-9162-abd56afc03f0"
   },
   "outputs": [],
   "source": [
    "def compute_rope_params(head_dim, theta_base=10_000, context_length=4096, dtype=torch.float32):\n",
    "    assert head_dim % 2 == 0, \"Embedding dimension must be even\"\n",
    "\n",
    "    # Compute the inverse frequencies\n",
    "    inv_freq = 1.0 / (theta_base ** (torch.arange(0, head_dim, 2, dtype=dtype)[: (head_dim // 2)].float() / head_dim))\n",
    "\n",
    "    # Generate position indices\n",
    "    positions = torch.arange(context_length, dtype=dtype)\n",
    "\n",
    "    # Compute the angles\n",
    "    angles = positions.unsqueeze(1) * inv_freq.unsqueeze(0)  # Shape: (context_length, head_dim // 2)\n",
    "\n",
    "    # Expand angles to match the head_dim\n",
    "    angles = torch.cat([angles, angles], dim=1)  # Shape: (context_length, head_dim)\n",
    "\n",
    "    # Precompute sine and cosine\n",
    "    cos = torch.cos(angles)\n",
    "    sin = torch.sin(angles)\n",
    "\n",
    "    return cos, sin\n",
    "\n",
    "\n",
    "def apply_rope(x, cos, sin):\n",
    "    # x: (batch_size, num_heads, seq_len, head_dim)\n",
    "    batch_size, num_heads, seq_len, head_dim = x.shape\n",
    "    assert head_dim % 2 == 0, \"Head dimension must be even\"\n",
    "\n",
    "    # Split x into first half and second half\n",
    "    x1 = x[..., : head_dim // 2]  # First half\n",
    "    x2 = x[..., head_dim // 2 :]  # Second half\n",
    "\n",
    "    # Adjust sin and cos shapes\n",
    "    cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0)  # Shape: (1, 1, seq_len, head_dim)\n",
    "    sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)\n",
    "\n",
    "    # Apply the rotary transformation\n",
    "    rotated = torch.cat((-x2, x1), dim=-1)\n",
    "    x_rotated = (x * cos) + (rotated * sin)\n",
    "\n",
    "    # It's ok to use lower-precision after applying cos and sin rotation\n",
    "    return x_rotated.to(dtype=x.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8169ab5-f976-4222-a2e1-eb1cabf267cb",
   "metadata": {
    "id": "e8169ab5-f976-4222-a2e1-eb1cabf267cb"
   },
   "outputs": [],
   "source": [
    "class GroupedQueryAttention(nn.Module):\n",
    "    def __init__(\n",
    "        self, d_in, num_heads, num_kv_groups, head_dim=None, qk_norm=False, dtype=None\n",
    "    ):\n",
    "        super().__init__()\n",
    "        assert num_heads % num_kv_groups == 0, \"num_heads must be divisible by num_kv_groups\"\n",
    "\n",
    "        self.num_heads = num_heads\n",
    "        self.num_kv_groups = num_kv_groups\n",
    "        self.group_size = num_heads // num_kv_groups\n",
    "\n",
    "        if head_dim is None:\n",
    "            assert d_in % num_heads == 0, \"`d_in` must be divisible by `num_heads` if `head_dim` is not set\"\n",
    "            head_dim = d_in // num_heads\n",
    "\n",
    "        self.head_dim = head_dim\n",
    "        self.d_out = num_heads * head_dim\n",
    "\n",
    "        self.W_query = nn.Linear(d_in, self.d_out, bias=False, dtype=dtype)\n",
    "        self.W_key = nn.Linear(d_in, num_kv_groups * head_dim, bias=False, dtype=dtype)\n",
    "        self.W_value = nn.Linear(d_in, num_kv_groups * head_dim, bias=False, dtype=dtype)\n",
    "\n",
    "        self.out_proj = nn.Linear(self.d_out, d_in, bias=False, dtype=dtype)\n",
    "\n",
    "        if qk_norm:\n",
    "            self.q_norm = RMSNorm(head_dim, eps=1e-6)\n",
    "            self.k_norm = RMSNorm(head_dim, eps=1e-6)\n",
    "        else:\n",
    "            self.q_norm = self.k_norm = None\n",
    "\n",
    "    def forward(self, x, mask, cos, sin):\n",
    "        b, num_tokens, _ = x.shape\n",
    "\n",
    "        # Apply projections\n",
    "        queries = self.W_query(x)  # (b, num_tokens, num_heads * head_dim)\n",
    "        keys = self.W_key(x)       # (b, num_tokens, num_kv_groups * head_dim)\n",
    "        values = self.W_value(x)   # (b, num_tokens, num_kv_groups * head_dim)\n",
    "\n",
    "        # Reshape\n",
    "        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim).transpose(1, 2)\n",
    "        keys = keys.view(b, num_tokens, self.num_kv_groups, self.head_dim).transpose(1, 2)\n",
    "        values = values.view(b, num_tokens, self.num_kv_groups, self.head_dim).transpose(1, 2)\n",
    "\n",
    "        # Optional normalization\n",
    "        if self.q_norm:\n",
    "            queries = self.q_norm(queries)\n",
    "        if self.k_norm:\n",
    "            keys = self.k_norm(keys)\n",
    "\n",
    "        # Apply RoPE\n",
    "        queries = apply_rope(queries, cos, sin)\n",
    "        keys = apply_rope(keys, cos, sin)\n",
    "\n",
    "        # Expand K and V to match number of heads\n",
    "        keys = keys.repeat_interleave(self.group_size, dim=1)\n",
    "        values = values.repeat_interleave(self.group_size, dim=1)\n",
    "\n",
    "        # Attention\n",
    "        attn_scores = queries @ keys.transpose(2, 3)\n",
    "        attn_scores = attn_scores.masked_fill(mask, -torch.inf)\n",
    "        attn_weights = torch.softmax(attn_scores / self.head_dim**0.5, dim=-1)\n",
    "\n",
    "        context = (attn_weights @ values).transpose(1, 2).reshape(b, num_tokens, self.d_out)\n",
    "        return self.out_proj(context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "457cb2f8-50c1-4045-8a74-f181bfb5fea9",
   "metadata": {
    "id": "457cb2f8-50c1-4045-8a74-f181bfb5fea9"
   },
   "outputs": [],
   "source": [
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "        self.att = GroupedQueryAttention(\n",
    "            d_in=cfg[\"emb_dim\"],\n",
    "            num_heads=cfg[\"n_heads\"],\n",
    "            head_dim=cfg[\"head_dim\"],\n",
    "            num_kv_groups=cfg[\"n_kv_groups\"],\n",
    "            qk_norm=cfg[\"qk_norm\"],\n",
    "            dtype=cfg[\"dtype\"]\n",
    "        )\n",
    "        self.ff = FeedForward(cfg)\n",
    "        self.norm1 = RMSNorm(cfg[\"emb_dim\"], eps=1e-6)\n",
    "        self.norm2 = RMSNorm(cfg[\"emb_dim\"], eps=1e-6)\n",
    "\n",
    "    def forward(self, x, mask, cos, sin):\n",
    "        # Shortcut connection for attention block\n",
    "        shortcut = x\n",
    "        x = self.norm1(x)\n",
    "        x = self.att(x, mask, cos, sin)  # Shape [batch_size, num_tokens, emb_size]\n",
    "        x = x + shortcut  # Add the original input back\n",
    "\n",
    "        # Shortcut connection for feed-forward block\n",
    "        shortcut = x\n",
    "        x = self.norm2(x)\n",
    "        x = self.ff(x)\n",
    "        x = x + shortcut  # Add the original input back\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e88de3e3-9f07-42cc-816b-28dbd46e96c4",
   "metadata": {
    "id": "e88de3e3-9f07-42cc-816b-28dbd46e96c4"
   },
   "outputs": [],
   "source": [
    "class Qwen3Model(nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super().__init__()\n",
    "\n",
    "        # Main model parameters\n",
    "        self.tok_emb = nn.Embedding(cfg[\"vocab_size\"], cfg[\"emb_dim\"], dtype=cfg[\"dtype\"])\n",
    "\n",
    "        self.trf_blocks = nn.ModuleList(  # ModuleList since Sequential can only accept one input, and we need `x, mask, cos, sin`\n",
    "            [TransformerBlock(cfg) for _ in range(cfg[\"n_layers\"])]\n",
    "        )\n",
    "\n",
    "        self.final_norm = RMSNorm(cfg[\"emb_dim\"])\n",
    "        self.out_head = nn.Linear(cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False, dtype=cfg[\"dtype\"])\n",
    "\n",
    "        # Reusuable utilities\n",
    "        if cfg[\"head_dim\"] is None:\n",
    "            head_dim = cfg[\"emb_dim\"] // cfg[\"n_heads\"]\n",
    "        else:\n",
    "            head_dim = cfg[\"head_dim\"]\n",
    "        cos, sin = compute_rope_params(\n",
    "            head_dim=head_dim,\n",
    "            theta_base=cfg[\"rope_base\"],\n",
    "            context_length=cfg[\"context_length\"]\n",
    "        )\n",
    "        self.register_buffer(\"cos\", cos, persistent=False)\n",
    "        self.register_buffer(\"sin\", sin, persistent=False)\n",
    "        self.cfg = cfg\n",
    "\n",
    "\n",
    "    def forward(self, in_idx):\n",
    "        # Forward pass\n",
    "        tok_embeds = self.tok_emb(in_idx)\n",
    "        x = tok_embeds\n",
    "\n",
    "        num_tokens = x.shape[1]\n",
    "        mask = torch.triu(torch.ones(num_tokens, num_tokens, device=x.device, dtype=torch.bool), diagonal=1)\n",
    "        \n",
    "        for block in self.trf_blocks:\n",
    "            x = block(x, mask, self.cos, self.sin)\n",
    "        x = self.final_norm(x)\n",
    "        logits = self.out_head(x.to(self.cfg[\"dtype\"]))\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2d201f-74ad-4d63-ab9c-601b00674a48",
   "metadata": {
    "id": "be2d201f-74ad-4d63-ab9c-601b00674a48"
   },
   "source": [
    "&nbsp;\n",
    "# 2. Initialize model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "caa142fa-b375-4e78-b392-2072ced666f3",
   "metadata": {
    "id": "caa142fa-b375-4e78-b392-2072ced666f3"
   },
   "outputs": [],
   "source": [
    "CHOOSE_MODEL = \"0.6B\"\n",
    "\n",
    "if CHOOSE_MODEL == \"0.6B\":\n",
    "    QWEN3_CONFIG = {\n",
    "        \"vocab_size\": 151_936,           # Vocabulary size\n",
    "        \"context_length\": 40_960,        # Context length that was used to train the model\n",
    "        \"emb_dim\": 1024,                 # Embedding dimension\n",
    "        \"n_heads\": 16,                   # Number of attention heads\n",
    "        \"n_layers\": 28,                  # Number of layers\n",
    "        \"hidden_dim\": 3072,              # Size of the intermediate dimension in FeedForward\n",
    "        \"head_dim\": 128,                 # Size of the heads in GQA\n",
    "        \"qk_norm\": True,                 # Whether to normalize queries and keys in GQA\n",
    "        \"n_kv_groups\": 8,                # Key-Value groups for grouped-query attention\n",
    "        \"rope_base\": 1_000_000.0,        # The base in RoPE's \"theta\"\n",
    "        \"dtype\": torch.bfloat16,         # Lower-precision dtype to reduce memory usage\n",
    "    }\n",
    "\n",
    "elif CHOOSE_MODEL == \"1.7B\":\n",
    "    QWEN3_CONFIG = {\n",
    "        \"vocab_size\": 151_936,\n",
    "        \"context_length\": 40_960,\n",
    "        \"emb_dim\": 2048,                 # 2x larger than above\n",
    "        \"n_heads\": 16,\n",
    "        \"n_layers\": 28,\n",
    "        \"hidden_dim\": 6144,              # 2x larger than above\n",
    "        \"head_dim\": 128,\n",
    "        \"qk_norm\": True,\n",
    "        \"n_kv_groups\": 8,\n",
    "        \"rope_base\": 1_000_000.0,\n",
    "        \"dtype\": torch.bfloat16,\n",
    "    }   \n",
    "\n",
    "elif CHOOSE_MODEL == \"4B\":\n",
    "    QWEN3_CONFIG = {\n",
    "        \"vocab_size\": 151_936,\n",
    "        \"context_length\": 40_960,\n",
    "        \"emb_dim\": 2560,                 # 25% larger than above\n",
    "        \"n_heads\": 32,                   # 2x larger than above\n",
    "        \"n_layers\": 36,                  # 29% larger than above\n",
    "        \"hidden_dim\": 9728,              # ~3x larger than above\n",
    "        \"head_dim\": 128,\n",
    "        \"qk_norm\": True,\n",
    "        \"n_kv_groups\": 8,\n",
    "        \"rope_base\": 1_000_000.0,\n",
    "        \"dtype\": torch.bfloat16,\n",
    "    }  \n",
    "\n",
    "elif CHOOSE_MODEL == \"8B\":\n",
    "    QWEN3_CONFIG = {\n",
    "        \"vocab_size\": 151_936,\n",
    "        \"context_length\": 40_960,\n",
    "        \"emb_dim\": 4096,                 # 60% larger than above\n",
    "        \"n_heads\": 32,\n",
    "        \"n_layers\": 36,                  # 26% larger than above\n",
    "        \"hidden_dim\": 12288,\n",
    "        \"head_dim\": 128,\n",
    "        \"qk_norm\": True,\n",
    "        \"n_kv_groups\": 8,\n",
    "        \"rope_base\": 1_000_000.0,\n",
    "        \"dtype\": torch.bfloat16,\n",
    "    } \n",
    "\n",
    "elif CHOOSE_MODEL == \"14B\":\n",
    "    QWEN3_CONFIG = {\n",
    "        \"vocab_size\": 151_936,\n",
    "        \"context_length\": 40_960,\n",
    "        \"emb_dim\": 5120,                 # 25% larger than above\n",
    "        \"n_heads\": 40,                   # 25% larger than above\n",
    "        \"n_layers\": 40,                  # 11% larger than above\n",
    "        \"hidden_dim\": 17408,             # 42% larger than above\n",
    "        \"head_dim\": 128,\n",
    "        \"qk_norm\": True,\n",
    "        \"n_kv_groups\": 8,\n",
    "        \"rope_base\": 1_000_000.0,\n",
    "        \"dtype\": torch.bfloat16,\n",
    "    } \n",
    "\n",
    "elif CHOOSE_MODEL == \"32B\":\n",
    "    QWEN3_CONFIG = {\n",
    "        \"vocab_size\": 151_936,\n",
    "        \"context_length\": 40_960,\n",
    "        \"emb_dim\": 5120,                \n",
    "        \"n_heads\": 64,                   # 60% larger than above\n",
    "        \"n_layers\": 64,                  # 60% larger than above\n",
    "        \"hidden_dim\": 25600,             # 47% larger than above\n",
    "        \"head_dim\": 128,\n",
    "        \"qk_norm\": True,\n",
    "        \"n_kv_groups\": 8,\n",
    "        \"rope_base\": 1_000_000.0,\n",
    "        \"dtype\": torch.bfloat16,\n",
    "    } \n",
    "\n",
    "else:\n",
    "    raise ValueError(f\"{CHOOSE_MODEL} is not supported.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "156253fe-aacd-4da2-8f13-705f05c4b11e",
   "metadata": {
    "id": "156253fe-aacd-4da2-8f13-705f05c4b11e"
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(123)\n",
    "model = Qwen3Model(QWEN3_CONFIG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eaf86265-4e9d-4024-9ed0-99076944e304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Qwen3Model(\n",
       "  (tok_emb): Embedding(151936, 1024)\n",
       "  (trf_blocks): ModuleList(\n",
       "    (0-27): 28 x TransformerBlock(\n",
       "      (att): GroupedQueryAttention(\n",
       "        (W_query): Linear(in_features=1024, out_features=2048, bias=False)\n",
       "        (W_key): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "        (W_value): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "        (out_proj): Linear(in_features=2048, out_features=1024, bias=False)\n",
       "        (q_norm): RMSNorm()\n",
       "        (k_norm): RMSNorm()\n",
       "      )\n",
       "      (ff): FeedForward(\n",
       "        (fc1): Linear(in_features=1024, out_features=3072, bias=False)\n",
       "        (fc2): Linear(in_features=1024, out_features=3072, bias=False)\n",
       "        (fc3): Linear(in_features=3072, out_features=1024, bias=False)\n",
       "      )\n",
       "      (norm1): RMSNorm()\n",
       "      (norm2): RMSNorm()\n",
       "    )\n",
       "  )\n",
       "  (final_norm): RMSNorm()\n",
       "  (out_head): Linear(in_features=1024, out_features=151936, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90aca91d-4bee-45ce-993a-4ec5393abe2b",
   "metadata": {},
   "source": [
    "- A quick check that the forward pass works before continuing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "adf0a6b7-b688-42c9-966e-c223d34db99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.2256, -0.0164, -0.7070,  ...,  0.4414,  0.1245,  1.0703],\n",
       "         [-0.6602,  0.5352, -0.0718,  ..., -0.0737,  0.5391,  0.3086],\n",
       "         [-0.4785, -0.1562,  0.1045,  ..., -0.2324,  0.2354,  0.6328]]],\n",
       "       dtype=torch.bfloat16, grad_fn=<UnsafeViewBackward0>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(torch.tensor([1, 2, 3]).unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "364e76ca-52f8-4fa5-af37-c4069f9694bc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "364e76ca-52f8-4fa5-af37-c4069f9694bc",
    "outputId": "00d7e983-262e-4c65-f322-f4d999311988"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of parameters: 751,632,384\n",
      "\n",
      "Total number of unique parameters: 596,049,920\n"
     ]
    }
   ],
   "source": [
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "print(f\"Total number of parameters: {total_params:,}\")\n",
    "\n",
    "# Account for weight tying\n",
    "total_params_normalized = total_params - model.tok_emb.weight.numel()\n",
    "print(f\"\\nTotal number of unique parameters: {total_params_normalized:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd5efb03-5a07-46e8-8607-93ed47549d2b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fd5efb03-5a07-46e8-8607-93ed47549d2b",
    "outputId": "65c1a95e-b502-4150-9e2e-da619d9053d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float32 (PyTorch default): 5.64 GB\n",
      "bfloat16: 2.82 GB\n"
     ]
    }
   ],
   "source": [
    "def model_memory_size(model, input_dtype=torch.float32):\n",
    "    total_params = 0\n",
    "    total_grads = 0\n",
    "    for param in model.parameters():\n",
    "        # Calculate total number of elements per parameter\n",
    "        param_size = param.numel()\n",
    "        total_params += param_size\n",
    "        # Check if gradients are stored for this parameter\n",
    "        if param.requires_grad:\n",
    "            total_grads += param_size\n",
    "\n",
    "    # Calculate buffer size (non-parameters that require memory)\n",
    "    total_buffers = sum(buf.numel() for buf in model.buffers())\n",
    "\n",
    "    # Size in bytes = (Number of elements) * (Size of each element in bytes)\n",
    "    # We assume parameters and gradients are stored in the same type as input dtype\n",
    "    element_size = torch.tensor(0, dtype=input_dtype).element_size()\n",
    "    total_memory_bytes = (total_params + total_grads + total_buffers) * element_size\n",
    "\n",
    "    # Convert bytes to gigabytes\n",
    "    total_memory_gb = total_memory_bytes / (1024**3)\n",
    "\n",
    "    return total_memory_gb\n",
    "\n",
    "print(f\"float32 (PyTorch default): {model_memory_size(model, input_dtype=torch.float32):.2f} GB\")\n",
    "print(f\"bfloat16: {model_memory_size(model, input_dtype=torch.bfloat16):.2f} GB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "31f12baf-f79b-499f-85c0-51328a6a20f5",
   "metadata": {
    "id": "31f12baf-f79b-499f-85c0-51328a6a20f5"
   },
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "elif torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "\n",
    "model.to(device);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c172f89f-d301-439f-b809-46169e5f5945",
   "metadata": {
    "id": "c172f89f-d301-439f-b809-46169e5f5945"
   },
   "source": [
    "&nbsp;\n",
    "# 4. Load pretrained weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75166128-5899-4995-9b88-9672e135650e",
   "metadata": {
    "id": "75166128-5899-4995-9b88-9672e135650e"
   },
   "outputs": [],
   "source": [
    "def load_weights_into_qwen(model, param_config, params):\n",
    "    def assign(left, right, tensor_name=\"unknown\"):\n",
    "        if left.shape != right.shape:\n",
    "            raise ValueError(f\"Shape mismatch in tensor '{tensor_name}'. Left: {left.shape}, Right: {right.shape}\")\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            if isinstance(right, torch.Tensor):\n",
    "                left.copy_(right)\n",
    "            else:\n",
    "                left.copy_(torch.as_tensor(right, dtype=left.dtype, device=left.device))\n",
    "    \n",
    "        return left \n",
    "\n",
    "    model.tok_emb.weight = assign(model.tok_emb.weight, params[\"model.embed_tokens.weight\"], \"model.embed_tokens.weight\")\n",
    "\n",
    "    for l in range(param_config[\"n_layers\"]):\n",
    "        block = model.trf_blocks[l]\n",
    "        att = block.att\n",
    "\n",
    "        # Q, K, V projections\n",
    "        att.W_query.weight = assign(\n",
    "            att.W_query.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.q_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.q_proj.weight\"\n",
    "        )\n",
    "        att.W_key.weight = assign(\n",
    "            att.W_key.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.k_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.k_proj.weight\"\n",
    "        )\n",
    "        att.W_value.weight = assign(\n",
    "            att.W_value.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.v_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.v_proj.weight\"\n",
    "        )\n",
    "\n",
    "        # Output projection\n",
    "        att.out_proj.weight = assign(\n",
    "            att.out_proj.weight,\n",
    "            params[f\"model.layers.{l}.self_attn.o_proj.weight\"],\n",
    "            f\"model.layers.{l}.self_attn.o_proj.weight\"\n",
    "        )\n",
    "\n",
    "        # QK norms\n",
    "        if hasattr(att, \"q_norm\") and att.q_norm is not None:\n",
    "            att.q_norm.scale = assign(\n",
    "                att.q_norm.scale,\n",
    "                params[f\"model.layers.{l}.self_attn.q_norm.weight\"],\n",
    "                f\"model.layers.{l}.self_attn.q_norm.weight\"\n",
    "            )\n",
    "        if hasattr(att, \"k_norm\") and att.k_norm is not None:\n",
    "            att.k_norm.scale = assign(\n",
    "                att.k_norm.scale,\n",
    "                params[f\"model.layers.{l}.self_attn.k_norm.weight\"],\n",
    "                f\"model.layers.{l}.self_attn.k_norm.weight\"\n",
    "            )\n",
    "\n",
    "        # Attention layernorm\n",
    "        block.norm1.scale = assign(\n",
    "            block.norm1.scale,\n",
    "            params[f\"model.layers.{l}.input_layernorm.weight\"],\n",
    "            f\"model.layers.{l}.input_layernorm.weight\"\n",
    "        )\n",
    "\n",
    "        # Feedforward weights\n",
    "        block.ff.fc1.weight = assign(\n",
    "            block.ff.fc1.weight,\n",
    "            params[f\"model.layers.{l}.mlp.gate_proj.weight\"],\n",
    "            f\"model.layers.{l}.mlp.gate_proj.weight\"\n",
    "        )\n",
    "        block.ff.fc2.weight = assign(\n",
    "            block.ff.fc2.weight,\n",
    "            params[f\"model.layers.{l}.mlp.up_proj.weight\"],\n",
    "            f\"model.layers.{l}.mlp.up_proj.weight\"\n",
    "        )\n",
    "        block.ff.fc3.weight = assign(\n",
    "            block.ff.fc3.weight,\n",
    "            params[f\"model.layers.{l}.mlp.down_proj.weight\"],\n",
    "            f\"model.layers.{l}.mlp.down_proj.weight\"\n",
    "        )\n",
    "        block.norm2.scale = assign(\n",
    "            block.norm2.scale,\n",
    "            params[f\"model.layers.{l}.post_attention_layernorm.weight\"],\n",
    "            f\"model.layers.{l}.post_attention_layernorm.weight\"\n",
    "        )\n",
    "\n",
    "    # Final normalization and output head\n",
    "    model.final_norm.scale = assign(model.final_norm.scale, params[\"model.norm.weight\"], \"model.norm.weight\")\n",
    "\n",
    "    if \"lm_head.weight\" in params:\n",
    "        model.out_head.weight = assign(model.out_head.weight, params[\"lm_head.weight\"], \"lm_head.weight\")\n",
    "    else:\n",
    "        model.out_head.weight = model.tok_emb.weight\n",
    "        print(\"Model uses weight tying.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "699cb1b8-a67d-49fb-80a6-0dad9d81f392",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "9881b6995c3f49dc89e6992fd9ab660b",
      "17a3174e65c54476b2e0d1faf8f011ca",
      "1bbf2e62c0754d1593beb4105a7f1ac1",
      "b82112e1dec645d98aa1c1ba64abcb61",
      "271e2bd6a35e4a8b92de8697f7c0be5f",
      "90a79523187446dfa692723b2e5833a7",
      "431ffb83b8c14bf182f0430e07ea6154",
      "a8f1b72a33dd4b548de23fbd95e0da18",
      "25cc36132d384189acfbecc59483134b",
      "bfd06423ad544218968648016e731a46",
      "d029630b63ff44cf807ade428d2eb421"
     ]
    },
    "id": "699cb1b8-a67d-49fb-80a6-0dad9d81f392",
    "outputId": "55b2f28c-142f-4698-9d23-d27456d3ed6d"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from pathlib import Path\n",
    "from safetensors.torch import load_file\n",
    "from huggingface_hub import hf_hub_download, snapshot_download\n",
    "\n",
    "\n",
    "if USE_REASONING_MODEL or USE_INSTRUCT_MODEL:\n",
    "    repo_id = f\"Qwen/Qwen3-{CHOOSE_MODEL}\"\n",
    "else:\n",
    "    repo_id = f\"Qwen/Qwen3-{CHOOSE_MODEL}-Base\"\n",
    "\n",
    "local_dir = Path(repo_id).parts[-1]\n",
    "\n",
    "if CHOOSE_MODEL == \"0.6B\":\n",
    "    weights_file = hf_hub_download(\n",
    "        repo_id=repo_id,\n",
    "        filename=\"model.safetensors\",\n",
    "        local_dir=local_dir,\n",
    "    )\n",
    "    weights_dict = load_file(weights_file)\n",
    "else:\n",
    "    repo_dir = snapshot_download(repo_id=repo_id, local_dir=local_dir)\n",
    "    index_path = os.path.join(repo_dir, \"model.safetensors.index.json\")\n",
    "    with open(index_path, \"r\") as f:\n",
    "        index = json.load(f)\n",
    "\n",
    "    weights_dict = {}\n",
    "    for filename in set(index[\"weight_map\"].values()):\n",
    "        shard_path = os.path.join(repo_dir, filename)\n",
    "        shard = load_file(shard_path)\n",
    "        weights_dict.update(shard)\n",
    "\n",
    "load_weights_into_qwen(model, QWEN3_CONFIG, weights_dict)\n",
    "model.to(device)\n",
    "del weights_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b345491-3510-4397-92d3-cd0a3fa3deee",
   "metadata": {},
   "source": [
    "&nbsp;\n",
    "# 4. Load tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b68ab489-48e5-471e-a814-56cda2d60f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from tokenizers import Tokenizer\n",
    "\n",
    "class Qwen3Tokenizer:\n",
    "    _SPECIALS = [\n",
    "        \"<|endoftext|>\",\n",
    "        \"<|im_start|>\", \"<|im_end|>\",\n",
    "        \"<|object_ref_start|>\", \"<|object_ref_end|>\",\n",
    "        \"<|box_start|>\", \"<|box_end|>\",\n",
    "        \"<|quad_start|>\", \"<|quad_end|>\",\n",
    "        \"<|vision_start|>\", \"<|vision_end|>\",\n",
    "        \"<|vision_pad|>\", \"<|image_pad|>\", \"<|video_pad|>\",\n",
    "        \"<think>\", \"</think>\"\n",
    "    ]\n",
    "    _SPLIT_RE = re.compile(r\"(<\\|[^>]+?\\|>|<think>|</think>)\")\n",
    "\n",
    "    def __init__(self, tokenizer_file_path=\"tokenizer.json\", repo_id=None,\n",
    "                 apply_chat_template=True, add_generation_prompt=False, add_thinking=False):\n",
    "\n",
    "        self.apply_chat_template = apply_chat_template\n",
    "        self.add_generation_prompt = add_generation_prompt\n",
    "        self.add_thinking = add_thinking\n",
    "\n",
    "        tok_file = Path(tokenizer_file_path)\n",
    "        self._tok = Tokenizer.from_file(str(tok_file))\n",
    "        self._special_to_id = {}\n",
    "        for t in self._SPECIALS:\n",
    "            tid = self._tok.token_to_id(t)\n",
    "            if tid is not None:\n",
    "                self._special_to_id[t] = tid\n",
    "\n",
    "        self.pad_token_id = self._special_to_id[\"<|endoftext|>\"]\n",
    "        self.eos_token_id = self.pad_token_id\n",
    "\n",
    "        if repo_id and \"Base\" not in repo_id:\n",
    "            eos_token = \"<|im_end|>\"\n",
    "        else:\n",
    "            eos_token = \"<|endoftext|>\"\n",
    "        if eos_token in self._special_to_id:\n",
    "            self.eos_token_id = self._special_to_id[eos_token]\n",
    "\n",
    "    def encode(self, text, chat_wrapped=None):\n",
    "        if chat_wrapped is None:\n",
    "            chat_wrapped = self.apply_chat_template\n",
    "\n",
    "        stripped = text.strip()\n",
    "        if stripped in self._special_to_id and \"\\n\" not in stripped:\n",
    "            return [self._special_to_id[stripped]]\n",
    "\n",
    "        if chat_wrapped:\n",
    "            text = self._wrap_chat(text)\n",
    "\n",
    "        ids = []\n",
    "        for part in filter(None, self._SPLIT_RE.split(text)):\n",
    "            if part in self._special_to_id:\n",
    "                ids.append(self._special_to_id[part])\n",
    "            else:\n",
    "                ids.extend(self._tok.encode(part).ids)\n",
    "        return ids\n",
    "\n",
    "    def decode(self, ids):\n",
    "        return self._tok.decode(ids, skip_special_tokens=False)\n",
    "\n",
    "    def _wrap_chat(self, user_msg):\n",
    "        s = f\"<|im_start|>user\\n{user_msg}<|im_end|>\\n\"\n",
    "        if self.add_generation_prompt:\n",
    "            s += \"<|im_start|>assistant\"\n",
    "            if self.add_thinking:\n",
    "                s += \"\\n\"\n",
    "            else:\n",
    "                s += \"\\n<think>\\n\\n</think>\\n\\n\"\n",
    "        return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7b6df8bc-7308-468e-93ce-2d5529ea7866",
   "metadata": {},
   "outputs": [],
   "source": [
    "if USE_REASONING_MODEL:\n",
    "    tokenizer_file_path = f\"Qwen3-{CHOOSE_MODEL}/tokenizer.json\"\n",
    "else:\n",
    "    tokenizer_file_path = f\"Qwen3-{CHOOSE_MODEL}-Base/tokenizer.json\"\n",
    "\n",
    "hf_hub_download(\n",
    "    repo_id=repo_id,\n",
    "    filename=\"tokenizer.json\",\n",
    "    local_dir=local_dir,\n",
    ")\n",
    "\n",
    "if USE_REASONING_MODEL or USE_INSTRUCT_MODEL:\n",
    "    tokenizer = Qwen3Tokenizer(\n",
    "        tokenizer_file_path=tokenizer_file_path,\n",
    "        repo_id=repo_id,\n",
    "        apply_chat_template=True,\n",
    "        add_generation_prompt=True,\n",
    "        add_thinking=USE_REASONING_MODEL\n",
    "    )\n",
    "\n",
    "else:\n",
    "    tokenizer = Qwen3Tokenizer(\n",
    "        tokenizer_file_path=tokenizer_file_path,\n",
    "        repo_id=repo_id,\n",
    "        apply_chat_template=False,\n",
    "        add_generation_prompt=False,\n",
    "        add_thinking=False\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1946b534-e3af-431a-a222-391a60bfa892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<|im_start|>user\\nGive me a short introduction to large language models.<|im_end|>\\n<|im_start|>assistant\\n'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = \"Give me a short introduction to large language models.\"\n",
    "\n",
    "input_token_ids = tokenizer.encode(prompt)\n",
    "text = tokenizer.decode(input_token_ids)\n",
    "text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d07df1-4401-4792-b549-7c4cc5632323",
   "metadata": {
    "id": "57d07df1-4401-4792-b549-7c4cc5632323"
   },
   "source": [
    "&nbsp;\n",
    "# 5. Generate text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b8401c6-e244-4cb7-9849-2ba71ce758d5",
   "metadata": {
    "id": "7b8401c6-e244-4cb7-9849-2ba71ce758d5"
   },
   "outputs": [],
   "source": [
    "def generate_text_basic_stream(model, token_ids, max_new_tokens, eos_token_id=None):\n",
    "\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for _ in range(max_new_tokens):\n",
    "            out = model(token_ids)[:, -1]\n",
    "            next_token = torch.argmax(out, dim=-1, keepdim=True)\n",
    "\n",
    "            if (eos_token_id is not None\n",
    "                   and torch.all(next_token == eos_token_id)):\n",
    "               break\n",
    "\n",
    "            yield next_token\n",
    "            \n",
    "            token_ids = torch.cat([token_ids, next_token], dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1c7a04fa-6aac-416b-8f63-f1e19227633d",
   "metadata": {
    "id": "1c7a04fa-6aac-416b-8f63-f1e19227633d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<think>\n",
      "Okay, the user wants a short introduction to large language models. Let me start by recalling what I know. Large language models are AI systems that can understand and generate human language. They're trained on massive datasets, so they can learn complex patterns and nuances.\n",
      "\n",
      "I should mention their ability to understand and generate text, not just specific tasks. Maybe include examples like chatbots or content generation. Also, emphasize their adaptability and efficiency. Oh, and maybe touch on their applications in various fields. Let me check if I'm covering all key points without being too technical. Keep it concise, around a sentence or two. Make sure it's clear and easy to understand.\n",
      "</think>\n",
      "\n",
      "Large language models (LLMs) are AI systems designed to understand and generate human language, enabling tasks like text generation, translation, and content creation. They are trained on vast datasets, allowing them to learn complex patterns and nuances, making them versatile for applications in various domains."
     ]
    }
   ],
   "source": [
    "input_token_ids_tensor = torch.tensor(input_token_ids, device=device).unsqueeze(0)\n",
    "\n",
    "\n",
    "for token in generate_text_basic_stream(\n",
    "    model=model,\n",
    "    token_ids=input_token_ids_tensor,\n",
    "    max_new_tokens=500,\n",
    "    eos_token_id=tokenizer.eos_token_id\n",
    "):\n",
    "    token_id = token.squeeze(0).tolist()\n",
    "    print(\n",
    "        tokenizer.decode(token_id),\n",
    "        end=\"\",\n",
    "        flush=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549324d6-5c71-4147-ae21-2e67675faa3d",
   "metadata": {
    "id": "549324d6-5c71-4147-ae21-2e67675faa3d"
   },
   "source": [
    "&nbsp;\n",
    "# What's next?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6edaaae-2de1-406c-8ffa-897cdfa3808c",
   "metadata": {
    "id": "e6edaaae-2de1-406c-8ffa-897cdfa3808c"
   },
   "source": [
    "- Check out the [README.md](./README.md), to use this model via the `llms_from_scratch` package\n",
    "- For those interested in a comprehensive guide on building a large language model from scratch and gaining a deeper understanding of its mechanics, you might like my [Build a Large Language Model (From Scratch)](http://mng.bz/orYv)\n",
    "\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "A100",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
